






from __future__ import annotations

import copy
import inspect
import unittest

import numpy as np

from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import (
       TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
       TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
       TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
       TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
       TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
       LayoutLMvaConfig,
       TFLayoutLMvaForQuestionAnswering,
       TFLayoutLMvaForSequenceClassification,
       TFLayoutLMvaForTokenClassification,
       TFLayoutLMvaModel,
   )

if is_vision_available():
   from PIL import Image

   from transformers import LayoutLMvaImageProcessor



class 				_A			:
    """simple docstring"""

    def __init__(				self      :     int ,     __SCREAMING_SNAKE_CASE      :     List[Any] ,     __SCREAMING_SNAKE_CASE      :     int=2 ,     __SCREAMING_SNAKE_CASE      :     Any=3 ,     __SCREAMING_SNAKE_CASE      :     Tuple=4 ,     __SCREAMING_SNAKE_CASE      :     List[Any]=2 ,     __SCREAMING_SNAKE_CASE      :     Any=7 ,     __SCREAMING_SNAKE_CASE      :     str=True ,     __SCREAMING_SNAKE_CASE      :     Optional[int]=True ,     __SCREAMING_SNAKE_CASE      :     Optional[int]=True ,     __SCREAMING_SNAKE_CASE      :     List[Any]=True ,     __SCREAMING_SNAKE_CASE      :     str=99 ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any]=36 ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any]=2 ,     __SCREAMING_SNAKE_CASE      :     List[Any]=4 ,     __SCREAMING_SNAKE_CASE      :     List[str]=37 ,     __SCREAMING_SNAKE_CASE      :     Optional[int]="gelu" ,     __SCREAMING_SNAKE_CASE      :     List[str]=0.1 ,     __SCREAMING_SNAKE_CASE      :     List[Any]=0.1 ,     __SCREAMING_SNAKE_CASE      :     Optional[Any]=512 ,     __SCREAMING_SNAKE_CASE      :     List[str]=16 ,     __SCREAMING_SNAKE_CASE      :     Optional[int]=2 ,     __SCREAMING_SNAKE_CASE      :     List[str]=0.02 ,     __SCREAMING_SNAKE_CASE      :     Optional[int]=6 ,     __SCREAMING_SNAKE_CASE      :     str=6 ,     __SCREAMING_SNAKE_CASE      :     Tuple=3 ,     __SCREAMING_SNAKE_CASE      :     List[str]=4 ,     __SCREAMING_SNAKE_CASE      :     Tuple=None ,     __SCREAMING_SNAKE_CASE      :     List[Any]=1000 ,     )      ->							Any:
           __UpperCAmelCase     				=parent
           __UpperCAmelCase     				=batch_size
           __UpperCAmelCase     				=num_channels
           __UpperCAmelCase     				=image_size
           __UpperCAmelCase     				=patch_size
           __UpperCAmelCase     				=is_training
           __UpperCAmelCase     				=use_input_mask
           __UpperCAmelCase     				=use_token_type_ids
           __UpperCAmelCase     				=use_labels
           __UpperCAmelCase     				=vocab_size
           __UpperCAmelCase     				=hidden_size
           __UpperCAmelCase     				=num_hidden_layers
           __UpperCAmelCase     				=num_attention_heads
           __UpperCAmelCase     				=intermediate_size
           __UpperCAmelCase     				=hidden_act
           __UpperCAmelCase     				=hidden_dropout_prob
           __UpperCAmelCase     				=attention_probs_dropout_prob
           __UpperCAmelCase     				=max_position_embeddings
           __UpperCAmelCase     				=type_vocab_size
           __UpperCAmelCase     				=type_sequence_label_size
           __UpperCAmelCase     				=initializer_range
           __UpperCAmelCase     				=coordinate_size
           __UpperCAmelCase     				=shape_size
           __UpperCAmelCase     				=num_labels
           __UpperCAmelCase     				=num_choices
           __UpperCAmelCase     				=scope
           __UpperCAmelCase     				=range_bbox

           # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
           __UpperCAmelCase     				=text_seq_length
           __UpperCAmelCase     				=(image_size // patch_size) ** 2 + 1
           __UpperCAmelCase     				=self.text_seq_length + self.image_seq_length



    def _a							(				self      :     int      )      ->							Optional[Any]:
           __UpperCAmelCase     				=ids_tensor([self.batch_size, self.text_seq_length] ,     self.vocab_size      )

           __UpperCAmelCase     				=ids_tensor([self.batch_size, self.text_seq_length, 4] ,     self.range_bbox      )
           __UpperCAmelCase     				=bbox.numpy()
           # Ensure that bbox is legal
           for i in range(bbox.shape[0]      ):
                  for j in range(bbox.shape[1]      ):
                         if bbox[i, j, 3] < bbox[i, j, 1]:
                                __UpperCAmelCase     				=bbox[i, j, 3]
                                __UpperCAmelCase     				=bbox[i, j, 1]
                                __UpperCAmelCase     				=tmp_coordinate
                         if bbox[i, j, 2] < bbox[i, j, 0]:
                                __UpperCAmelCase     				=bbox[i, j, 2]
                                __UpperCAmelCase     				=bbox[i, j, 0]
                                __UpperCAmelCase     				=tmp_coordinate
           __UpperCAmelCase     				=tf.constant(__SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

           __UpperCAmelCase     				=None
           if self.use_input_mask:
                  __UpperCAmelCase     				=random_attention_mask([self.batch_size, self.text_seq_length]      )

           __UpperCAmelCase     				=None
           if self.use_token_type_ids:
                  __UpperCAmelCase     				=ids_tensor([self.batch_size, self.text_seq_length] ,     self.type_vocab_size      )

           __UpperCAmelCase     				=None
           __UpperCAmelCase     				=None
           if self.use_labels:
                  __UpperCAmelCase     				=ids_tensor([self.batch_size] ,     self.type_sequence_label_size      )
                  __UpperCAmelCase     				=ids_tensor([self.batch_size, self.text_seq_length] ,     self.num_labels      )

           __UpperCAmelCase     				=LayoutLMvaConfig(
               vocab_size=self.vocab_size ,     hidden_size=self.hidden_size ,     num_hidden_layers=self.num_hidden_layers ,     num_attention_heads=self.num_attention_heads ,     intermediate_size=self.intermediate_size ,     hidden_act=self.hidden_act ,     hidden_dropout_prob=self.hidden_dropout_prob ,     attention_probs_dropout_prob=self.attention_probs_dropout_prob ,     max_position_embeddings=self.max_position_embeddings ,     type_vocab_size=self.type_vocab_size ,     initializer_range=self.initializer_range ,     coordinate_size=self.coordinate_size ,     shape_size=self.shape_size ,     input_size=self.image_size ,     patch_size=self.patch_size ,     )

           return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels



    def _a							(				self      :     int ,     __SCREAMING_SNAKE_CASE      :     Any ,     __SCREAMING_SNAKE_CASE      :     str ,     __SCREAMING_SNAKE_CASE      :     Tuple ,     __SCREAMING_SNAKE_CASE      :     List[str] ,     __SCREAMING_SNAKE_CASE      :     Optional[int] ,     __SCREAMING_SNAKE_CASE      :     int      )      ->							Tuple:
           __UpperCAmelCase     				=TFLayoutLMvaModel(config=__SCREAMING_SNAKE_CASE      )

           # text + image
           __UpperCAmelCase     				=model(__SCREAMING_SNAKE_CASE ,     pixel_values=__SCREAMING_SNAKE_CASE ,     training=__SCREAMING_SNAKE_CASE      )
           __UpperCAmelCase     				=model(
               __SCREAMING_SNAKE_CASE ,     bbox=__SCREAMING_SNAKE_CASE ,     pixel_values=__SCREAMING_SNAKE_CASE ,     attention_mask=__SCREAMING_SNAKE_CASE ,     token_type_ids=__SCREAMING_SNAKE_CASE ,     training=__SCREAMING_SNAKE_CASE ,     )
           __UpperCAmelCase     				=model(__SCREAMING_SNAKE_CASE ,     bbox=__SCREAMING_SNAKE_CASE ,     pixel_values=__SCREAMING_SNAKE_CASE ,     training=__SCREAMING_SNAKE_CASE      )

           self.parent.assertEqual(result.last_hidden_state.shape ,     (self.batch_size, self.seq_length, self.hidden_size)      )

           # text only
           __UpperCAmelCase     				=model(__SCREAMING_SNAKE_CASE ,     training=__SCREAMING_SNAKE_CASE      )
           self.parent.assertEqual(
               result.last_hidden_state.shape ,     (self.batch_size, self.text_seq_length, self.hidden_size)      )

           # image only
           __UpperCAmelCase     				=model({"""pixel_values""": pixel_values} ,     training=__SCREAMING_SNAKE_CASE      )
           self.parent.assertEqual(
               result.last_hidden_state.shape ,     (self.batch_size, self.image_seq_length, self.hidden_size)      )



    def _a							(				self      :     str ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any] ,     __SCREAMING_SNAKE_CASE      :     Any ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any] ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any] ,     __SCREAMING_SNAKE_CASE      :     List[Any] ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any] ,     __SCREAMING_SNAKE_CASE      :     Dict      )      ->							List[str]:
           __UpperCAmelCase     				=self.num_labels
           __UpperCAmelCase     				=TFLayoutLMvaForSequenceClassification(config=__SCREAMING_SNAKE_CASE      )
           __UpperCAmelCase     				=model(
               __SCREAMING_SNAKE_CASE ,     bbox=__SCREAMING_SNAKE_CASE ,     pixel_values=__SCREAMING_SNAKE_CASE ,     attention_mask=__SCREAMING_SNAKE_CASE ,     token_type_ids=__SCREAMING_SNAKE_CASE ,     labels=__SCREAMING_SNAKE_CASE ,     training=__SCREAMING_SNAKE_CASE ,     )
           self.parent.assertEqual(result.logits.shape ,     (self.batch_size, self.num_labels)      )



    def _a							(				self      :     List[str] ,     __SCREAMING_SNAKE_CASE      :     Any ,     __SCREAMING_SNAKE_CASE      :     Tuple ,     __SCREAMING_SNAKE_CASE      :     str ,     __SCREAMING_SNAKE_CASE      :     Tuple ,     __SCREAMING_SNAKE_CASE      :     Any ,     __SCREAMING_SNAKE_CASE      :     Optional[int] ,     __SCREAMING_SNAKE_CASE      :     Optional[int]      )      ->							List[Any]:
           __UpperCAmelCase     				=self.num_labels
           __UpperCAmelCase     				=TFLayoutLMvaForTokenClassification(config=__SCREAMING_SNAKE_CASE      )
           __UpperCAmelCase     				=model(
               __SCREAMING_SNAKE_CASE ,     bbox=__SCREAMING_SNAKE_CASE ,     pixel_values=__SCREAMING_SNAKE_CASE ,     attention_mask=__SCREAMING_SNAKE_CASE ,     token_type_ids=__SCREAMING_SNAKE_CASE ,     labels=__SCREAMING_SNAKE_CASE ,     training=__SCREAMING_SNAKE_CASE ,     )
           self.parent.assertEqual(result.logits.shape ,     (self.batch_size, self.text_seq_length, self.num_labels)      )



    def _a							(				self      :     Optional[int] ,     __SCREAMING_SNAKE_CASE      :     Tuple ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any] ,     __SCREAMING_SNAKE_CASE      :     Dict ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any] ,     __SCREAMING_SNAKE_CASE      :     Dict ,     __SCREAMING_SNAKE_CASE      :     int ,     __SCREAMING_SNAKE_CASE      :     Any      )      ->							str:
           __UpperCAmelCase     				=2
           __UpperCAmelCase     				=TFLayoutLMvaForQuestionAnswering(config=__SCREAMING_SNAKE_CASE      )
           __UpperCAmelCase     				=model(
               __SCREAMING_SNAKE_CASE ,     bbox=__SCREAMING_SNAKE_CASE ,     pixel_values=__SCREAMING_SNAKE_CASE ,     attention_mask=__SCREAMING_SNAKE_CASE ,     token_type_ids=__SCREAMING_SNAKE_CASE ,     start_positions=__SCREAMING_SNAKE_CASE ,     end_positions=__SCREAMING_SNAKE_CASE ,     training=__SCREAMING_SNAKE_CASE ,     )
           self.parent.assertEqual(result.start_logits.shape ,     (self.batch_size, self.seq_length)      )
           self.parent.assertEqual(result.end_logits.shape ,     (self.batch_size, self.seq_length)      )



    def _a							(				self      :     str      )      ->							Optional[int]:
           __UpperCAmelCase     				=self.prepare_config_and_inputs()
           ((__UpperCAmelCase)				, (__UpperCAmelCase)				, (__UpperCAmelCase)				, (__UpperCAmelCase)				, (__UpperCAmelCase)				, (__UpperCAmelCase)				, (__UpperCAmelCase)				, (__UpperCAmelCase))     				=config_and_inputs
           __UpperCAmelCase     				={
               """input_ids""": input_ids,
               """bbox""": bbox,
               """pixel_values""": pixel_values,
               """token_type_ids""": token_type_ids,
               """attention_mask""": input_mask,
           }
           return config, inputs_dict




@require_tf
class 				_A			(     UpperCamelCase					,					UpperCamelCase					,					unittest.TestCase				):
    """simple docstring"""

    lowerCamelCase	:							List[str]				= (
        (
            TFLayoutLMvaModel,
            TFLayoutLMvaForQuestionAnswering,
            TFLayoutLMvaForSequenceClassification,
            TFLayoutLMvaForTokenClassification,
        )
        if is_tf_available()
        else ()
    )
    lowerCamelCase	:							Tuple				= (
        {'document-question-answering': TFLayoutLMvaForQuestionAnswering, 'feature-extraction': TFLayoutLMvaModel}
        if is_tf_available()
        else {}
    )

    lowerCamelCase	:							Any				= False
    lowerCamelCase	:							Optional[Any]				= False
    lowerCamelCase	:							Any				= False



    def _a							(				self      :     Tuple ,     __SCREAMING_SNAKE_CASE      :     Tuple ,     __SCREAMING_SNAKE_CASE      :     Any ,     __SCREAMING_SNAKE_CASE      :     List[Any] ,     __SCREAMING_SNAKE_CASE      :     int ,     __SCREAMING_SNAKE_CASE      :     Dict      )      ->							str:
           return True



    def _a							(				self      :     Optional[int] ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any] ,     __SCREAMING_SNAKE_CASE      :     List[Any] ,     __SCREAMING_SNAKE_CASE      :     Tuple=False      )      ->							dict:
           __UpperCAmelCase     				=copy.deepcopy(__SCREAMING_SNAKE_CASE      )

           if model_class in get_values(__SCREAMING_SNAKE_CASE      ):
                  __UpperCAmelCase     				={
                      k: tf.tile(tf.expand_dims(__SCREAMING_SNAKE_CASE ,     1      ) ,     (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)      )
                      if isinstance(__SCREAMING_SNAKE_CASE ,     tf.Tensor      ) and v.ndim > 0
                      else v
                      for k, v in inputs_dict.items()
                  }

           if return_labels:
                  if model_class in get_values(__SCREAMING_SNAKE_CASE      ):
                         __UpperCAmelCase     				=tf.ones(self.model_tester.batch_size ,     dtype=tf.intaa      )
                  elif model_class in get_values(__SCREAMING_SNAKE_CASE      ):
                         __UpperCAmelCase     				=tf.zeros(self.model_tester.batch_size ,     dtype=tf.intaa      )
                         __UpperCAmelCase     				=tf.zeros(self.model_tester.batch_size ,     dtype=tf.intaa      )
                  elif model_class in get_values(__SCREAMING_SNAKE_CASE      ):
                         __UpperCAmelCase     				=tf.zeros(self.model_tester.batch_size ,     dtype=tf.intaa      )
                  elif model_class in get_values(__SCREAMING_SNAKE_CASE      ):
                         __UpperCAmelCase     				=tf.zeros(
                             (self.model_tester.batch_size, self.model_tester.text_seq_length) ,     dtype=tf.intaa      )

           return inputs_dict



    def _a							(				self      :     Optional[int]      )      ->							Optional[int]:
           __UpperCAmelCase     				=TFLayoutLMvaModelTester(self      )
           __UpperCAmelCase     				=ConfigTester(self ,     config_class=__SCREAMING_SNAKE_CASE ,     hidden_size=37      )



    def _a							(				self      :     Tuple      )      ->							List[str]:
           self.config_tester.run_common_tests()



    def _a							(				self      :     Any      )      ->							Any:
           __UpperCAmelCase				, __UpperCAmelCase     				=self.model_tester.prepare_config_and_inputs_for_common()
           for model_class in self.all_model_classes:
                  __UpperCAmelCase     				=model_class(__SCREAMING_SNAKE_CASE      )
                  if getattr(__SCREAMING_SNAKE_CASE ,     """hf_compute_loss""" ,     __SCREAMING_SNAKE_CASE      ):
                         # The number of elements in the loss should be the same as the number of elements in the label
                         __UpperCAmelCase     				=self._prepare_for_class(inputs_dict.copy() ,     __SCREAMING_SNAKE_CASE ,     return_labels=__SCREAMING_SNAKE_CASE      )
                         __UpperCAmelCase     				=prepared_for_class[
                             sorted(prepared_for_class.keys() - inputs_dict.keys() ,     reverse=__SCREAMING_SNAKE_CASE      )[0]
                         ]
                         __UpperCAmelCase     				=added_label.shape.as_list()[:1]

                         # Test that model correctly compute the loss with kwargs
                         __UpperCAmelCase     				=self._prepare_for_class(inputs_dict.copy() ,     __SCREAMING_SNAKE_CASE ,     return_labels=__SCREAMING_SNAKE_CASE      )
                         __UpperCAmelCase     				=prepared_for_class.pop("""input_ids"""      )

                         __UpperCAmelCase     				=model(__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      )[0]
                         self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]      )

                         # Test that model correctly compute the loss when we mask some positions
                         __UpperCAmelCase     				=self._prepare_for_class(inputs_dict.copy() ,     __SCREAMING_SNAKE_CASE ,     return_labels=__SCREAMING_SNAKE_CASE      )
                         __UpperCAmelCase     				=prepared_for_class.pop("""input_ids"""      )
                         if "labels" in prepared_for_class:
                                __UpperCAmelCase     				=prepared_for_class["""labels"""].numpy()
                                if len(labels.shape      ) > 1 and labels.shape[1] != 1:
                                       __UpperCAmelCase     				=-100
                                       __UpperCAmelCase     				=tf.convert_to_tensor(__SCREAMING_SNAKE_CASE      )
                                       __UpperCAmelCase     				=model(__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      )[0]
                                       self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]      )
                                       self.assertTrue(not np.any(np.isnan(loss.numpy()      )      )      )

                # Test that model correctly compute the loss with a dict
                         __UpperCAmelCase     				=self._prepare_for_class(inputs_dict.copy() ,     __SCREAMING_SNAKE_CASE ,     return_labels=__SCREAMING_SNAKE_CASE      )
                         __UpperCAmelCase     				=model(__SCREAMING_SNAKE_CASE      )[0]
                         self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]      )

                         # Test that model correctly compute the loss with a tuple
                         __UpperCAmelCase     				=self._prepare_for_class(inputs_dict.copy() ,     __SCREAMING_SNAKE_CASE ,     return_labels=__SCREAMING_SNAKE_CASE      )

                         # Get keys that were added with the _prepare_for_class function
                         __UpperCAmelCase     				=prepared_for_class.keys() - inputs_dict.keys()
                         __UpperCAmelCase     				=inspect.signature(model.call      ).parameters
                         __UpperCAmelCase     				=list(signature.keys()      )

                         # Create a dictionary holding the location of the tensors in the tuple
                         __UpperCAmelCase     				={0: """input_ids"""}
                         for label_key in label_keys:
                                __UpperCAmelCase     				=signature_names.index(__SCREAMING_SNAKE_CASE      )
                                __UpperCAmelCase     				=label_key
                         __UpperCAmelCase     				=sorted(tuple_index_mapping.items()      )
                         # Initialize a list with their default values, update the values and convert to a tuple
                         __UpperCAmelCase     				=[]

                         for name in signature_names:
                                if name != "kwargs":
                                       list_input.append(signature[name].default      )

                         for index, value in sorted_tuple_index_mapping:
                                __UpperCAmelCase     				=prepared_for_class[value]

                         __UpperCAmelCase     				=tuple(__SCREAMING_SNAKE_CASE      )

                         # Send to model
                         __UpperCAmelCase     				=model(tuple_input[:-1]      )[0]

                         self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]      )



    def _a							(				self      :     Tuple      )      ->							Optional[int]:
           (
               (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, 
           )     				=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     Optional[Any]      )      ->							Tuple:
           (
               (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, 
           )     				=self.model_tester.prepare_config_and_inputs()
           for type in ["absolute", "relative_key", "relative_key_query"]:
                  __UpperCAmelCase     				=type
                  self.model_tester.create_and_check_model(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     List[Any]      )      ->							Tuple:
           (
               (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, 
           )     				=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(
               __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     str      )      ->							Any:
           (
               (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, 
           )     				=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(
               __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     List[Any]      )      ->							int:
           (
               (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, 
           )     				=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(
               __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      )



    @slow
    def _a							(				self      :     List[str]      )      ->							Any:
           for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  __UpperCAmelCase     				=TFLayoutLMvaModel.from_pretrained(__SCREAMING_SNAKE_CASE      )
                  self.assertIsNotNone(__SCREAMING_SNAKE_CASE      )







def       lowercase__	(					)	->				Optional[Any]:







       """simple docstring"""






       __UpperCAmelCase     				=Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
       return image





@require_tf
class 				_A			(     unittest.TestCase				):
    """simple docstring"""

    @cached_property
    def _a							(				self      :     Tuple      )      ->							str:
           return LayoutLMvaImageProcessor(apply_ocr=__SCREAMING_SNAKE_CASE      ) if is_vision_available() else None



    @slow
    def _a							(				self      :     str      )      ->							Optional[Any]:
           __UpperCAmelCase     				=TFLayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base"""      )

           __UpperCAmelCase     				=self.default_image_processor
           __UpperCAmelCase     				=prepare_img()
           __UpperCAmelCase     				=image_processor(images=__SCREAMING_SNAKE_CASE ,     return_tensors="""tf"""      ).pixel_values

           __UpperCAmelCase     				=tf.constant([[1, 2]]      )
           __UpperCAmelCase     				=tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]]      ) ,     axis=0      )

           # forward pass
           __UpperCAmelCase     				=model(input_ids=__SCREAMING_SNAKE_CASE ,     bbox=__SCREAMING_SNAKE_CASE ,     pixel_values=__SCREAMING_SNAKE_CASE ,     training=__SCREAMING_SNAKE_CASE      )

           # verify the logits
           __UpperCAmelCase     				=(1, 199, 768)
           self.assertEqual(outputs.last_hidden_state.shape ,     __SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				=tf.constant(
               [[-0.0_529, 0.3_618, 0.1_632], [-0.1_587, -0.1_667, -0.0_400], [-0.1_557, -0.1_671, -0.0_505]]      )

           self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3] ,     __SCREAMING_SNAKE_CASE ,     atol=1e-4      )      )












"""simple docstring"""





_SCREAMING_SNAKE_CASE   :							Optional[int]          =		8.314_4598







def 	lowerCamelCase__			(				_lowerCamelCase			:						float						,							_lowerCamelCase			:						float					) ->							float:
  if temperature < 0:
    raise Exception('Temperature cannot be less than 0 K'					)
  if molar_mass <= 0:
    raise Exception('Molar mass cannot be less than or equal to 0 kg/mol'					)
  else:
    return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5


if __name__ == "__main__":
  import doctest

  # run doctest
  doctest.testmod()

  # example
  _SCREAMING_SNAKE_CASE   :							List[Any]          =		300
  _SCREAMING_SNAKE_CASE   :							Dict          =		28
  _SCREAMING_SNAKE_CASE   :							List[str]          =		rms_speed_of_molecule(temperature, molar_mass)
  print(F'''Vrms of Nitrogen gas at 300 K is {vrms} m/s''')




